To overcome the performance limitation imposed by the requirement to use a small adaptation gain in the gradient-based scheme of [1]} we add to the filtering technique of [2]}, [1]}, a novel mathematical concept: the creation of a virtual invariant manifold. Thanks to this new technique we are able to replace the gradient-based observer design by the high performance KRE-based estimator. As explained in [4]} the key step in Kreisselmeier's estimator is the construction of the KRE that, as shown in [5]}, is a particular form of the more general dynamic regressor extension. For the purpose of this paper, the key feature of this estimator is that, in contrast with gradient or least-squares schemes, it is possible to make the estimator converge arbitrarily fast (after a short transient phase due to filtering) by increasing the adaptation gain—see also [6]} for a detailed analysis of the properties of this estimator.
